AVERAGE COST OPTIMALITY INEQUALITY FOR MARKOV DECISION PROCESSES WITH BOREL SPACES AND UNIVERSALLY MEASURABLE POLICIES

被引:4
|
作者
Yu, Huizhen [1 ]
机构
[1] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2N8, Canada
关键词
Markov decision processes; Borel spaces; universally measurable policies; average cost; optimality inequality; majorization conditions; OPTIMAL REWARD OPERATOR; EQUATION; ITERATION; CONVERGENCE; THEOREMS; CHAINS;
D O I
10.1137/19M1239507
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We consider average-cost Markov decision processes (MDPs) with Borel state and action spaces and universally measurable policies. For the nonnegative cost model and an unbounded cost model with a Lyapunov-type stability character, we introduce a set of new conditions under which we prove the average cost optimality inequality (ACOI) via the vanishing discount factor approach. Unlike most existing results on the ACOI, our result does not require any compactness and continuity conditions on the MDPs. Instead, the main idea is to use the almost-uniform-convergence property of a pointwise convergent sequence of measurable functions as asserted in Egoroff's theorem. Our conditions are formulated in order to exploit this property. Among others, we require that for each state, on selected subsets of actions at that state, the state transition stochastic kernel is majorized by finite measures. We combine this majorization property of the transition kernel with Egoroff's theorem to prove the ACOI.
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页码:2469 / 2502
页数:34
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